Near-Optimal Detection for Both Data and Sneak-Path Interference in Resistive Memories with Random Cell Selector Failures

TL;DR

This paper introduces a near-optimal detection scheme for resistive memories that effectively manages data and sneak-path interference by exploiting inter-cell correlations, significantly improving detection performance.

Contribution

It presents a novel joint detection method that approaches optimal performance and handles large arrays with linear complexity, unlike previous sub-optimal schemes.

Findings

Achieves near-optimal detection performance in resistive memories.

Effectively exploits inter-cell correlations for interference mitigation.

Maintains linear complexity suitable for large memory arrays.

Abstract

Resistive random-access memory is one of the most promising candidates for the next generation of non-volatile memory technology. However, its crossbar structure causes severe "sneak-path" interference, which also leads to strong inter-cell correlation. Recent works have mainly focused on sub-optimal data detection schemes by ignoring inter-cell correlation and treating sneak-path interference as independent noise. We propose a near-optimal data detection scheme that can approach the performance bound of the optimal detection scheme. Our detection scheme leverages a joint data and sneak-path interference recovery and can use all inter-cell correlations. The scheme is appropriate for data detection of large memory arrays with only linear operation complexity.